Analytics, Big Data, IT, & Information Systems Resources

Engaging with different types of resources will help you understand your industry on a deeper level.

Caution

When using resources on this guide, continue to evaluate credibility and relevance for your audience. A resource can be considered credible in one situation but questionable in another, or its quality can suddenly degrade. Additionally, credible resources can still contain problematic content like content marketing, inaccurate opinion pieces, flawed research, or misrepresented evidence.

Professional Organizations

Professional organizations and trade associations promote their industries and provide networking opportunities to members. Their websites offer information and educational materials for to professionals in the industry.

The following examples are organizations for professionals in data-related fields:

Trade Publications

Trade journals or trade magazines provide industry-specific insights, giving prospective professionals an insider’s view of the field.

The following examples are trade publications with a focus on data:

Popular Magazines

Popular magazines, although aimed at a general audience, can be useful for understanding an industry in a larger context or from a consumer’s point of view.

Popular magazines that might be useful to professionals in data-related fields include:

Note, this publication is ending it’s print version in the fall of 2026.

Newspapers

Newspapers cover current events relevant to professionals in all fields.

Newspapers that might have useful coverage for data scientists include:

New York Times

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Wall Street Journal

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Websites

The following sites are examples of resources that cover tech:

Podcasts

Most industries have reputable experts who podcast about their chosen field, providing an insider’s look. These types of information resources vary widely and require vetting, but can be credible.

The following resources offer useful perspectives on tech and data science:

Books

Most industries have notable books that insiders find useful or that have had an influence on that industry. Asking insiders is the best way to find out which books are considered essential in your field.

Here are a few examples of influential books related to data science:

The Algorithm Design Manual (Steven S. Skiena)
Atlas of AI: Power, Politics, and the Planetary Costs of Artificial Intelligence (Kate Crawford)
Co-Intelligence: Living and Working with AI (Ethan Mollick)
Enshittification: Why Everything Suddenly Got Worse and What to Do About It (Cory Doctorow)
The Thinking Machine: Jensen Huang, Nvidia, and the World’s Most Coveted Microchip (Stephen Witt)
Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy (Cathy O’Neil)

Scholarly Journals

Scholarly journals publish in depth research, usually conducted by professors.

The following resources offer perspectives on information and technology: